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Found 5 papers, 1 papers with code
MOSPAT: AutoML based Model Selection and Parameter Tuning for Time Series Anomaly Detection
Organizations leverage anomaly and changepoint detection algorithms to detect changes in user behavior or service availability and performance.
Self-supervised learning for fast and scalable time series hyper-parameter tuning
Hyper-parameters of time series models play an important role in time series analysis.
Transferable Feature Learning on Graphs Across Visual Domains
In this paper, we propose a novel Transferable Feature Learning approach on Graphs (TFLG) for unsupervised adversarial domain adaptation, which jointly incorporates sample-level and class-level structure information across two domains.
Co-embedding of Nodes and Edges with Graph Neural Networks
CensNet is a general graph embedding framework, which embeds both nodes and edges to a latent feature space.
CensNet: Convolution with Edge-Node Switching in Graph Neural Networks
In this paper, we present CensNet, Convolution with Edge-Node Switching graph neural network, for semi-supervised classification and regression in graph-structured data with both node and edge features.
Ranked #1 on
Graph Regression
on Tox21
